Actin Simulation Grinding Velocity Analysis

◌ General change in leading tip grinding data, as specie ratio changes (before and after filter z <= 0.12 uM):

In [1]:
# prepared by Sam. -- Prof. Nitta Lab.
%run preparation.py
%matplotlib inline

Conf0.1 shape before filter:  (301, 4)
Conf0.1 shape after filter:  (301, 4)
Conf0.15 shape before filter:  (301, 4)
Conf0.15 shape after filter:  (301, 4)
Conf0.2 shape before filter:  (301, 4)
Conf0.2 shape after filter:  (301, 4)
Conf0.25 shape before filter:  (301, 4)
Conf0.25 shape after filter:  (301, 4)
Conf0.3 shape before filter:  (301, 4)
Conf0.3 shape after filter:  (41, 4)
Conf0.35 shape before filter:  (301, 4)
Conf0.35 shape after filter:  (67, 4)
Conf0.4 shape before filter:  (301, 4)
Conf0.4 shape after filter:  (82, 4)
Conf0.45 shape before filter:  (301, 4)
Conf0.45 shape after filter:  (61, 4)
Conf0.5 shape before filter:  (301, 4)
Conf0.5 shape after filter:  (44, 4)
Conf0.55 shape before filter:  (301, 4)
Conf0.55 shape after filter:  (155, 4)
Conf0.6 shape before filter:  (301, 4)
Conf0.6 shape after filter:  (254, 4)
Conf0.65 shape before filter:  (301, 4)
Conf0.65 shape after filter:  (280, 4)
Conf0.7 shape before filter:  (301, 4)
Conf0.7 shape after filter:  (291, 4)
Conf0.75 shape before filter:  (301, 4)
Conf0.75 shape after filter:  (291, 4)
Conf0.8 shape before filter:  (301, 4)
Conf0.8 shape after filter:  (293, 4)
Conf0.85 shape before filter:  (301, 4)
Conf0.85 shape after filter:  (296, 4)
Conf0.9 shape before filter:  (301, 4)
Conf0.9 shape after filter:  (295, 4)
Conf0.95 shape before filter:  (301, 4)
Conf0.95 shape after filter:  (298, 4)
Conf1.0 shape before filter:  (301, 4)
Conf1.0 shape after filter:  (298, 4)

◌ Output while skipping rows [R0.10B13ATP2000MD3000]:

In [2]:
columns = ['time', 'x_tip', 'y_tip']
df_load = pd.read_csv('TipXY_A001_r0.1.txt', names=columns, delim_whitespace=True)
df_nice = df_load.drop(['time'], axis=1)
df = df_nice.iloc[0::10, :]
df.to_csv('SkippedTipXY_A001.csv', encoding='utf-8', index=False)
In [3]:
Dx_tip = np.diff(df['x_tip']); Dy_tip = np.diff(df['y_tip'])
DD=np.sqrt((Dx_tip**2)+(Dy_tip**2))
np.savetxt('DD_A001.csv', DD, delimiter=',')
v=DD/(10*dt); Av_vel = np.mean(v)
np.savetxt('v.csv', v, delimiter=',')
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)
In [4]:
plt.figure(figsize=(10,8))
#plt.text(6, 8, 'Av_Vel = ', fontdict=font); plt.text(8, 8, '%.5f'%Av_vel, fontdict=font)
#plt.text(6, 7, 'Vel_SD = ', fontdict=font); plt.text(8, 7, '%.5f'%vSD, fontdict=font)
plt.plot(df['x_tip'],df['y_tip'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Grinding'); plt.legend(loc='upper left'); plt.grid()
#plt.savefig('actin_graph.svg', format='svg', dpi=1200)
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 0.11145858362603829
VSD: 0.05837139284648545

◌ For clarity, the above output is as a result of skipping every 10 rows. However, it may not be the best representation of the **average** grinding velocity. In the following analysis, all leading tip datasets are included except if z is > 0.12 uM

Output while only considering the grinding velocity:
$0 \leq$ filament z-attach limit $\leq 0.12 \mu$ M

◌ Leading tip grinding analysis for R0.10B13ATP2000MD3000:

In [5]:
index_diff01 = np.diff(df01.index) # difference in index between one row and next
Dx = np.diff(df01['x']); Dy = np.diff(df01['y']) # value difference between one row and next
DD=np.sqrt((Dx**2)+(Dy**2)) # calculate distance 
v=DD/(index_diff01*dt); Av_vel = np.mean(v) # calculate velocity and mean
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD) # calculate standard deviation

plt.figure(figsize=(10,8))
plt.plot(df01['x'],df01['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Grinding'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 0.9264735240450269
VSD: 0.6265233020031812
In [6]:
df01_ = df01.drop(df01.index[300]) # drop the last row to match vel shape
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df01_['time'],df01_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df01_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df01_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [7]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df01['z'].hist(bins=100, ax=ax, color='green')
Out[7]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc93471c4a8>

◌ Leading tip grinding analysis for R0.15B13ATP2000MD3000:

In [8]:
index_diff015 = np.diff(df015.index) 
Dx = np.diff(df015['x']); Dy = np.diff(df015['y']) 
DD=np.sqrt((Dx**2)+(Dy**2)) 
v=DD/(index_diff015*dt); Av_vel = np.mean(v) 
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD) 

plt.figure(figsize=(10,8))
plt.plot(df015['x'],df015['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Grinding'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 3.406924525719324
VSD: 2.7961853675313817
In [9]:
df015_ = df015.drop(df015.index[300]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df015_['time'],df015_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df015_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df015_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [10]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df015['z'].hist(bins=100, ax=ax, color='green')
Out[10]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9345d3748>

◌ Leading tip grinding analysis for R0.20B13ATP2000MD3000:

In [11]:
index_diff02 = np.diff(df02.index)
Dx = np.diff(df02['x']); Dy = np.diff(df02['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff02*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df02['x'],df02['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 3.216799964173127
VSD: 3.0764013170929148
In [12]:
df02_ = df02.drop(df02.index[300])
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df02_['time'],df02_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df02_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df02_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [13]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df02['z'].hist(bins=100, ax=ax, color='green')
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc93420c6d8>

◌ Leading tip grinding analysis for R0.25B13ATP2000MD3000:

In [14]:
index_diff025 = np.diff(df025.index)
Dx = np.diff(df025['x']); Dy = np.diff(df025['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff025*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df025['x'],df025['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 1.0884400487231294
VSD: 0.8404796977848611
In [15]:
df025_ = df025.drop(df025.index[300])
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df025_['time'],df025_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df025_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df025_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [16]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df025['z'].hist(bins=100, ax=ax, color='green')
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9340426a0>

◌ Leading tip grinding analysis for R0.30B13ATP2000MD3000:

In [17]:
index_diff03 = np.diff(df03.index)
Dx = np.diff(df03['x']); Dy = np.diff(df03['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff03*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df03['x'],df03['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 9.50190705925856
VSD: 11.11691495802427
In [18]:
df03_ = df03.drop(df03.index[40]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df03_['time'],df03_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df03_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df03_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [19]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df03['z'].hist(bins=100, ax=ax, color='green')
Out[19]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc934547518>

◌ Leading tip grinding analysis for R0.35B13ATP2000MD3000:

In [20]:
index_diff035 = np.diff(df035.index)
Dx = np.diff(df035['x']); Dy = np.diff(df035['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff035*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df035['x'],df035['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 13.448639892409409
VSD: 11.546783285033328
In [21]:
df035_ = df035.drop(df035.index[66]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df035_['time'],df035_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df035_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df035_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [22]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df035['z'].hist(bins=100, ax=ax, color='green')
Out[22]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc92eee8fd0>

◌ Leading tip grinding analysis for R0.40B13ATP2000MD3000:

In [23]:
index_diff04 = np.diff(df04.index)
Dx = np.diff(df04['x']); Dy = np.diff(df04['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff04*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df04['x'],df04['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 12.409646552649724
VSD: 11.093747956924846
In [24]:
df04_ = df04.drop(df04.index[81]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df04_['time'],df04_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df04_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df04_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [25]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df04['z'].hist(bins=100, ax=ax, color='green')
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc91bb1d5f8>

◌ Leading tip grinding analysis for R0.45B13ATP2000MD3000:

In [26]:
index_diff045 = np.diff(df045.index)
Dx = np.diff(df045['x']); Dy = np.diff(df045['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff045*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df045['x'],df045['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 10.015138577365649
VSD: 8.229601963609156
In [27]:
df045_ = df045.drop(df045.index[60]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df045_['time'],df045_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df045_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df045_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [28]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df045['z'].hist(bins=100, ax=ax, color='green')
Out[28]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc92efd44e0>

◌ Leading tip grinding analysis for R0.50B13ATP2000MD3000:

In [29]:
index_diff05 = np.diff(df05.index)
Dx = np.diff(df05['x']); Dy = np.diff(df05['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff05*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df05['x'],df05['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 10.53953829421381
VSD: 8.713125283876595
In [30]:
df05_ = df05.drop(df05.index[43]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df05_['time'],df05_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df05_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df05_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [31]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df05['z'].hist(bins=100, ax=ax, color='green')
Out[31]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc929179470>

◌ Leading tip grinding analysis for R0.55B13ATP2000MD3000:

In [32]:
index_diff055 = np.diff(df055.index)
Dx = np.diff(df055['x']); Dy = np.diff(df055['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff055*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df055['x'],df055['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 13.527845856280615
VSD: 8.541105565939676
In [33]:
df055_ = df055.drop(df055.index[154]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df055_['time'],df055_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df055_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df055_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [34]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df055['z'].hist(bins=100, ax=ax, color='green')
Out[34]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc934273dd8>

◌ Leading tip grinding analysis for R0.60B13ATP2000MD3000:

In [35]:
index_diff06 = np.diff(df06.index)
Dx = np.diff(df06['x']); Dy = np.diff(df06['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff06*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df06['x'],df06['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 13.165139831936234
VSD: 8.141510154048126
In [36]:
df06_ = df06.drop(df06.index[253]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df06_['time'],df06_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df06_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df06_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [37]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df06['z'].hist(bins=100, ax=ax, color='green')
Out[37]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc934724e10>

◌ Leading tip grinding analysis for R0.65B13ATP2000MD3000:

In [38]:
index_diff065 = np.diff(df065.index)
Dx = np.diff(df065['x']); Dy = np.diff(df065['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff065*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df065['x'],df065['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 12.04318236805964
VSD: 7.306770348141472
In [39]:
df065_ = df065.drop(df065.index[279]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df065_['time'],df065_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df065_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df065_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [40]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df065['z'].hist(bins=100, ax=ax, color='green')
Out[40]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc928dd2278>

◌ Leading tip grinding analysis for R0.70B13ATP2000MD3000:

In [41]:
index_diff07 = np.diff(df07.index)
Dx = np.diff(df07['x']); Dy = np.diff(df07['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff07*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df07['x'],df07['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 12.33191415418844
VSD: 7.505000160488821
In [42]:
df07_ = df07.drop(df07.index[290]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df07_['time'],df07_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df07_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df07_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [43]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df07['z'].hist(bins=100, ax=ax, color='green')
Out[43]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc93452cef0>

◌ Leading tip grinding analysis for R0.75B13ATP2000MD3000:

In [44]:
index_diff075 = np.diff(df075.index)
Dx = np.diff(df075['x']); Dy = np.diff(df075['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff075*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df075['x'],df075['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 11.723131066566607
VSD: 7.1164650664182965
In [45]:
df075_ = df075.drop(df075.index[290]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df075_['time'],df075_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df075_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df075_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [46]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df075['z'].hist(bins=100, ax=ax, color='green')
Out[46]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc928c90048>

◌ Leading tip grinding analysis for R0.80B13ATP2000MD3000:

In [47]:
index_diff08 = np.diff(df08.index)
Dx = np.diff(df08['x']); Dy = np.diff(df08['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff08*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df08['x'],df08['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 11.691965618426297
VSD: 7.248935548162617
In [48]:
df08_ = df08.drop(df08.index[292]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df08_['time'],df08_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df08_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df08_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [49]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df08['z'].hist(bins=100, ax=ax, color='green')
Out[49]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9290e7898>

◌ Leading tip grinding analysis for R0.85B13ATP2000MD3000:

In [50]:
index_diff085 = np.diff(df085.index)
Dx = np.diff(df085['x']); Dy = np.diff(df085['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff085*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df085['x'],df085['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 10.876282259663984
VSD: 5.304207571905855
In [51]:
df085_ = df085.drop(df085.index[295]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df085_['time'],df085_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df085_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df085_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [52]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df085['z'].hist(bins=100, ax=ax, color='green')
Out[52]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9290e7ef0>

◌ Leading tip grinding analysis for R0.90B13ATP2000MD3000:

In [53]:
index_diff09 = np.diff(df09.index)
Dx = np.diff(df09['x']); Dy = np.diff(df09['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff09*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df09['x'],df09['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 11.316034120970297
VSD: 6.288090934188312
In [54]:
df09_ = df09.drop(df09.index[294]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df09_['time'],df09_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df09_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df09_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [55]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df09['z'].hist(bins=100, ax=ax, color='green')
Out[55]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9287899b0>

◌ Leading tip grinding analysis for R0.95B13ATP2000MD3000:

In [56]:
index_diff095 = np.diff(df095.index)
Dx = np.diff(df095['x']); Dy = np.diff(df095['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff095*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df095['x'],df095['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 10.762110292673217
VSD: 6.165490412821767
In [57]:
df095_ = df095.drop(df095.index[297]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df095_['time'],df095_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df095_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df095_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [58]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df095['z'].hist(bins=100, ax=ax, color='green')
Out[58]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc9284ae588>

◌ Leading tip grinding analysis for R1.00B13ATP2000MD3000:

In [59]:
index_diff10 = np.diff(df10.index)
Dx = np.diff(df10['x']); Dy = np.diff(df10['y'])
DD=np.sqrt((Dx**2)+(Dy**2))
v=DD/(index_diff10*dt); Av_vel = np.mean(v)
vSD=np.sum(((v-Av_vel)**2)/(np.size(v)-1)); vSD=np.sqrt(vSD)

plt.figure(figsize=(10,8))
plt.plot(df10['x'],df10['y'], label='Leading tip', color='green', marker='o', \
         linestyle='dashed', linewidth=2, markersize=7)
plt.xlabel('X', fontdict=font); plt.ylabel('Y', fontdict=font)
plt.title('Actin Filament Movement'); plt.legend(loc='upper left'); plt.grid()
print(colored('Ave_vel:', 'yellow', attrs=['reverse', 'blink']), \
      colored(Av_vel, 'yellow', attrs=['reverse', 'blink']))
print(colored('VSD:', 'yellow', attrs=['reverse', 'blink']), \
      colored(vSD, 'yellow', attrs=['reverse', 'blink']))

Ave_vel: 10.834874773271228
VSD: 5.646034608042509
In [60]:
df10_ = df10.drop(df10.index[297]) 
fig, ax1 = plt.subplots(dpi=500)
ax1.plot(df10_['time'],df10_['z'], 'b', label='Actin Z-variations')
ax1.axhline(df10_['z'].mean(), linestyle='--', linewidth=2, color='b', label='Average Z')
ax1.set_xlabel('Time (s)',fontdict=font)
ax1.set_ylabel('Z-Distance', color='b',fontdict=font)
ax1.tick_params('y', colors='b')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(df10_['time'],v, 'r', label='Tip velocity')
ax2.axhline(Av_vel, linestyle='--', linewidth=2, color='r', label='Average tip grinding velocity')
fig.set_size_inches([16, 10])
fig.tight_layout()
ax2.set_ylabel('Tip-Velocity', color='r',fontdict=font)
ax2.tick_params('y', colors='r')
plt.title('Actin Filament Grinding',fontdict=font); plt.legend(loc='upper right')
plt.grid(); plt.show()
In [61]:
fig = plt.figure(figsize=(14,10), dpi=500)
ax = fig.gca()
ax.set_xlabel('Height Z')
ax.set_ylabel('Occurrence')
df10['z'].hist(bins=100, ax=ax, color='green')
Out[61]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc928b5b518>

Leading tip mean grinding velocity as specie ratio (R) varies, for B13ATP2000MD3000

◌ Grinding velocity changes:

In [62]:
column_names = ['r','b','atp','md','v','vsd']
gdta = pd.read_csv('grinding_vel.csv', names=column_names); gdta
Out[62]:
r b atp md v vsd
0 0.10 13 2000 3000 0.926474 0.626523
1 0.15 13 2000 3000 3.406925 2.796185
2 0.20 13 2000 3000 3.216800 3.076401
3 0.25 13 2000 3000 1.088440 0.840480
4 0.30 13 2000 3000 9.501907 11.116915
5 0.35 13 2000 3000 13.448640 11.546783
6 0.40 13 2000 3000 12.409647 11.093748
7 0.45 13 2000 3000 10.015139 8.229602
8 0.50 13 2000 3000 10.539538 8.713125
9 0.55 13 2000 3000 13.527846 8.541106
10 0.60 13 2000 3000 13.165140 8.141510
11 0.65 13 2000 3000 12.043182 7.306770
12 0.70 13 2000 3000 12.331914 7.505000
13 0.75 13 2000 3000 11.723131 7.116465
14 0.80 13 2000 3000 11.691966 7.248936
15 0.85 13 2000 3000 10.876282 5.304208
16 0.90 13 2000 3000 11.316034 6.288091
17 0.95 13 2000 3000 10.762110 6.165490
18 1.00 13 2000 3000 10.834875 5.646035
In [63]:
plt.figure(figsize=(14,10), dpi=500)
plt.errorbar(gdta['r'],gdta['v'],yerr=gdta['vsd'], ecolor='b', capsize=7, \
             mec='blue', color='green', marker='D', linewidth=2, \
             markersize=7, label='Mean Velocity Deviation: ATP = 2000, MD = 3000')
plt.xlabel('Specie Ratio', fontdict=font); plt.ylabel('Mean Velocity', fontdict=font)
plt.title('Actin Filament Grinding'); plt.legend(loc='upper left'); plt.grid()
In [ ]: